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RECSYS
2015
ACM

Predicting Online Performance of News Recommender Systems Through Richer Evaluation Metrics

3 years 6 months ago
Predicting Online Performance of News Recommender Systems Through Richer Evaluation Metrics
We investigate how metrics that can be measured offline can be used to predict the online performance of recommender systems, thus avoiding costly A-B testing. In addition to accuracy metrics, we combine diversity, coverage, and serendipity metrics to create a new performance model. Using the model, we quantify the trade-off between different metrics and propose to use it to tune the parameters of recommender algorithms without the need for online testing. Another application for the model is a self-adjusting algorithm blend that optimizes a recommender’s parameters over time. We evaluate our findings on data and experiments from news websites. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval—Information filtering Keywords recommender system;online evaluation;evaluation metrics
Andrii Maksai, Florent Garcin, Boi Faltings
Added 17 Apr 2016
Updated 17 Apr 2016
Type Journal
Year 2015
Where RECSYS
Authors Andrii Maksai, Florent Garcin, Boi Faltings
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